AI Studio & Data Platform
Integrated MLOps platform — feature stores, versioned datasets, notebook workspaces, pipeline orchestration, and inference endpoints in one environment. Closes the handoff gap between data prep, training, and deployment so the full AI lifecycle lives in one place.
Overview
AI projects fail in the seams — data lives in one tool, notebooks in another, model registry in a third, and deployment requires a handoff across three teams with no single owner of the end-to-end path. AI Studio and Data Platform collapses those seams into a single environment where data scientists and ML engineers discover data, build models, tune hyperparameters, register versions, and deploy to serving — all from one workspace. The platform integrates a feature store, experiment tracker (MLflow), model registry, pipeline orchestrator (Kubeflow), and GPU-backed notebook workspaces behind a unified control plane. Your team spends its time on the model and the data, not on stitching together point tools that were never designed to work as one.
Clevertek scopes every engagement to your environment — capacity, sites, compliance and support model — so you get a tailored plan rather than a fixed SKU. Pricing is quote-only, and our solutions architects will work through your requirements before any proposal.
Our approach
We deploy and operate an integrated AI development platform spanning data preparation, feature engineering, model training, experiment tracking, and production serving. The platform combines Jupyter-based notebook workspaces with GPU compute (NVIDIA A100, H100, H200, B200), a managed feature store for reusable transformations, MLflow for experiment provenance, Kubeflow Pipelines for workflow orchestration, and a model registry with automated deployment to inference endpoints. Every component is pre-integrated and tested as a release — not a DIY assembly of open-source projects. We manage the platform infrastructure, GPU scheduling, storage tiering, and user access. Your team connects through a single URL with their data, their notebooks, and their deployed models all in one place. Built on NVIDIA AI Enterprise, Kubeflow 1.9+, and MLflow 2.x.
Why work with us
One platform from data to deployment
Feature store, notebooks, experiment tracking, model registry, and inference serving in a single environment. No context-switching between tools, no handoff gaps between teams, no lost experiments.
GPU-backed from day one
NVIDIA A100, H100, H200, and B200 GPUs with NVLink and InfiniBand interconnects available for training and batch inference. GPU scheduling managed by Kubeflow with fair-share queueing across teams.
Pre-integrated, tested as a release
All components — Kubeflow, MLflow, Jupyter, feature store, serving stack — integrated and tested as a single release. No version conflicts, no dependency debugging, no pipeline breakage from mismatched library versions.
Reproducibility baked into every step
Every experiment captures the dataset version, code commit, hyperparameters, environment image, and model metrics. Reproduce any result with one command — not a notebook you hope runs the same way twice.
Key benefits
What this solution delivers for your business.
Eliminate AI toolchain fragmentation
Data scientists navigate four or five separate tools to go from idea to deployment. AI Studio replaces the chain with one workspace, eliminating context-switch overhead and lost work between tool boundaries.
Reproducible experiments every time
MLflow captures parameters, metrics, code version, data snapshot, and environment for every run. Reproduce any past experiment identically — no more notebooks that only work on the author machine.
Faster path from notebook to production
The model registry connects directly to inference serving with automated canary deployment, A/B traffic splitting, and rollback. A model proven in a notebook deploys to a production endpoint in the same session.
Collaborative team workflows
Shared feature store, versioned experiments, and collaborative workspaces mean the whole team works from the same foundation. No duplicated feature engineering, no overwritten notebooks, no siloed work.
What's included
Part of this managed service.
Unified AI workspace
Single web-based environment combining Jupyter notebooks, Kubeflow Pipelines, MLflow experiment tracking, feature store, model registry, and inference serving. All components authenticated through a single identity provider.
- JupyterLab with GPU-backed kernels
- Kubeflow Pipelines for workflow orchestration
- MLflow tracking server for experiment provenance
- Central model registry with version management
Managed feature store
Central repository for feature definitions, transformations, and serving with point-in-time correct retrieval. Features defined once and reused across experiments, with automated backfill for historical data.
- Feature definition and versioning
- Point-in-time correct retrieval
- Automated backfill pipelines
- Online and offline serving endpoints
Experiment tracking and provenance
MLflow-based tracking of parameters, metrics, code version, dataset snapshot, and environment image for every training run. Full lineage from raw data to deployed model version.
- Automatic parameter and metric logging
- Code and dataset version pinning
- Environment image capture per run
- Model lineage from training to deployment
GPU-accelerated training pipelines
Kubeflow Pipelines orchestrate multi-step ML workflows on GPU infrastructure. Distributed training with Horovod or PyTorch DDP, hyperparameter tuning with Katib, and automated model evaluation.
- Kubeflow Pipelines on GPU nodes
- Distributed training with PyTorch DDP
- Katib hyperparameter tuning
- Automated model evaluation gates
Where it helps
Real-world scenarios where this solution delivers measurable outcomes.
End-to-end ML lifecycle for a data science team
A team of 10 data scientists shares a feature store, experiment tracker, and model registry. Features are built once; experiments are reproducible; models graduate from notebook to production through the registry with canary deployment and automatic rollback.
AI initiative spanning multiple teams
Three teams — data engineering, ML research, and ML engineering — collaborate through one platform. Data engineers serve features through the store, researchers train and register models, and engineering deploys from the registry with automated CI/CD gates between stages.
Questions buyers actually ask
Do we have to move all our data into the platform?
No. The platform connects to data where it lives — object storage, data warehouses, data lakes — and brings only the needed slices into the feature store. We avoid a full data migration you did not ask for.
Can we bring our own models and frameworks?
Yes. The platform supports PyTorch, TensorFlow, JAX, and Scikit-learn with custom environment images. You are not locked into a framework; the platform adapts to your stack.
How is this different from using Kubeflow and MLflow separately?
Integration. Running Kubeflow, MLflow, Jupyter, and a feature store separately means you own every version conflict, authentication integration, and pipeline breakage. AI Studio delivers them as a tested, integrated release with a single support path.
Does this support MLOps and CI/CD for models?
Yes. The platform integrates with Git-based CI/CD pipelines for model deployment. Model registry events trigger automated canary deployments, A/B traffic splitting, and rollback on metric degradation.
Ready to scope a solution?
Talk to a Clevertek solutions architect about your requirements — no obligation.